#coding:utf-8 
import tensorflow as tf
import numpy as np
#一次喂如神经网络的数据
BATCH_SIZE = 8
seed = 23455

#基于seed产生随机数
rng = np.random.RandomState(seed)
#随机数返回32行 2列的矩阵， 表示 32组 体积和重量 作为输入数据集
X = rng.rand(32, 2)
#从X 32行 2列的数据中 取出1行 判断如果和小于1 给Y赋值1 如果和不小于1 给Y赋值0
#作为输入数据集的标签（正确答案）

Y = [[int(x0+x1<1)] for (x0,x1) in X]
print "X:\n", X
print "Y\n", Y

#1定义神经网络的输入、参数 和输出，定义前向传播过程
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))

W1 = tf.Variable(tf.random_normal([2,3], stddev=1, seed=1))
W2 = tf.Variable(tf.random_normal([3,1], stddev=1, seed=1))

a = tf.matmul(x, W1)
y = tf.matmul(a, W2)

#定义损失函数 及反响传播方法
loss = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
#tarin_step = tf.train.MomentumOptmizer(0.001,0,9).minimizer(loss)
#train_step = tf.train.AdamOptmizer(0.001).minimizer(loss)

#生成会话 训练STEPS轮
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    #输出目前（未经过训练的）参数取值
    print "w1:\n", sess.run(W1)
    print "w2:\n", sess.run(W2)
    print "\n"

    #训练模型
    STEPS = 3000
    for i in range(STEPS):
        start = (i*BATCH_SIZE)%32
        end = start + BATCH_SIZE
        sess.run(train_step, feed_dict={x:X, y_:Y})
        if i%500==0:
            total_loss = sess.run(loss, feed_dict={x:X, y_:Y})
            print("total_loss:\n", total_loss)

    print "\n"
    print "w1:", sess.run(W1)
    print "w2", sess.run(W2)